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Nico Bohlinger

@nicobohlinger.bsky.social
47 followers 42 following 62 posts

27 | Robotics, RL Research | Intern @ Amazon FAR in SF & PhD student at @ias-tudarmstadt.bsky.social

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Nico Bohlinger @nicobohlinger.bsky.social · 01/10/2026
⚡ One policy, millions of embodiments, over 200 robot models. Can we add yours? We're building γ₀, a generalist RL policy for motion control trained across millions of randomized embodiments derived from a growing collection of more than 200 robot models.
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Nico Bohlinger @nicobohlinger.bsky.social · 01/10/2026
⚡️ Happy to share CrossBFM, our new work on cross-embodiment Behavior Foundation Models! 🔗 Project page: dotandung.github.io/crossbfm/
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Nico Bohlinger @nicobohlinger.bsky.social · 30/09/2026
⚡️ A goal that is close in space can be far away in time ⏳ Introducing ChronoSRL: We give the critic of self-supervised RL a temporal geometry, so embedding distances measure the time to reach a goal 🤖 Play with the policy on quadruped task and more: nico-bohlinger.github.io/chronosrl_we...
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Nico Bohlinger @nicobohlinger.bsky.social · 12/09/2026
Happy to share that I joined Amazon FAR (Frontier AI & Robotics) in San Francisco as a Research Intern! 🦾 Really looking forward to exciting research around humanoids and reinforcement learning, under supervision of @carlosferrazza.bsky.social and Rocky Duan.
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Nico Bohlinger @nicobohlinger.bsky.social · 01/09/2026
⚡ In the last few weeks, I had the great chance to present our recent work on embodiment-aware reinforcement learning for robot control and design 🤖
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Reposted by Nico Bohlinger
Kai Ploeger @kaiploeger.bsky.social · 24/06/2026
Learning five-ball juggling on the second attempt, with two Barrett WAMs. Most humans take years of practice. Paper and videos: kai-ploeger.com/residual-juggling #robotics #juggling
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Nico Bohlinger @nicobohlinger.bsky.social · 16/06/2026
I had a great time visiting Prof. Kevin S. Luck at @vuamsterdam.bsky.social and to present our recent works on embodiment-aware learning for robot control and design! So much cool work here in Amsterdam about Robot Co-Design! There is huge potential in the data-driven way of designing robots!
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Nico Bohlinger @nicobohlinger.bsky.social · 01/06/2026
⚡️What if we could design robots with gradients? 🤖 Introducing Shape Your Body: we train one multi-embodiment policy + value function, then optimize new robot designs through value gradients. 🔗 Try out our interactive demo here: nico-bohlinger.github.io/shape-your-b...
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Nico Bohlinger @nicobohlinger.bsky.social · 20/10/2025
I'm presenting four different works at IROS 2025 this week in Hangzhou 🤖
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Nico Bohlinger @nicobohlinger.bsky.social · 02/10/2025
⚡️ Can one unified policy control 10 million different robots and zero-shot transfer to completely unseen robots, even humanoids? 🔗 Yes! Checkout our paper: arxiv.org/abs/2509.02815
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Nico Bohlinger @nicobohlinger.bsky.social · 30/09/2025
🇰🇷 Conferences are about finally meeting your collaborators from all around the world! Check out our work on Embodiment Scaling Laws @CoRL2025 We investigate cross-embodiment learning as the next axis of scaling for truly generalist policies 📈 🔗 All details: embodiment-scaling-laws.github.io
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Reposted by Nico Bohlinger
Antonin Raffin @araffin.bsky.social · 07/07/2025
Need for Speed or: How I Learned to Stop Worrying About Sample Efficiency Part II of my blog series "Getting SAC to Work on a Massive Parallel Simulator" is out! I've included everything I tried that didn't work (and why Jax PPO was different from PyTorch PPO) araffin.github.io/post/tune-sa...
araffin.github.io
Getting SAC to Work on a Massive Parallel Simulator: Tuning for Speed (Part II) | Antonin Raffin | Homepage
This second post details how I tuned the Soft-Actor Critic (SAC) algorithm to learn as fast as PPO in the context of a massively parallel simulator (thousands of robots simulated in parallel).
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Nico Bohlinger @nicobohlinger.bsky.social · 02/07/2025
Robot Randomization is fun!
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Nico Bohlinger @nicobohlinger.bsky.social · 13/06/2025
🚀 Checkout our new work at @rldmdublin2025.bsky.social today at poster#16! We're showing how to make Explicit Policy-conditioned Value Functions V(θ) (originating from Faccio & Schmidhuber) work for more complex control tasks. The secret? Massive scaling!
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Reposted by Nico Bohlinger
Intelligent Autonomous Systems @ias-tudarmstadt.bsky.social · 12/06/2025
IAS is at RLDM 2025! We have many exiting works to share (see 👇), so come to our posters and talk to us!
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Nico Bohlinger @nicobohlinger.bsky.social · 18/03/2025
⚡️ Do you think training robot locomotion needs large scale simulation? Think again! We train an omnidirectional locomotion policy directly on a real quadruped in just a few minutes 🚀 Top speeds of 0.85 m/s, two different control approaches, indoor and outdoor experiments, and more! 🤖🏃‍♂️
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Reposted by Nico Bohlinger
Antonin Raffin @araffin.bsky.social · 10/03/2025
"As researchers, we tend to publish only positive results, but I think a lot of valuable insights are lost in our unpublished failures." New blog post: Getting SAC to Work on a Massive Parallel Simulator (part I) araffin.github.io/post/sac-mas...
araffin.github.io
Getting SAC to Work on a Massive Parallel Simulator: An RL Journey With Off-Policy Algorithms (Part I) | Antonin Raffin | Homepage
This post details how I managed to get the Soft-Actor Critic (SAC) and other off-policy reinforcement learning algorithms to work on massively parallel simulators (think Isaac Sim with thousands of ro...
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